Multi-channel image fusion and visualization operations are useful for many applications. For example, multi-channel image fusion can be used to fuse multiple images with different respective exposures, and therefrom, generate a single image with a high dynamic range (HDR). In image fusion, one of the most important issues is how to preserve the salience from the sources. Since gradient convey important salient features, we conduct the fusion on gradients. However, traditional fusion methods based on gradient treat gradients from multi-channels as a multi-valued vector, and compute associated statistics under the assumption of identical distribution. In fact, different source channels may reflect different important salient features, and their gradients are basically non-identically distributed. This prevents existing methods from successful salience preservation.